Overview

Dataset statistics

Number of variables32
Number of observations206593
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory50.4 MiB
Average record size in memory256.0 B

Variable types

Numeric20
Categorical12

Warnings

id has a high cardinality: 206593 distinct values High cardinality
first_browser has a high cardinality: 52 distinct values High cardinality
df_index is highly correlated with year_first_active and 1 other fieldsHigh correlation
days_from_first_active_until_booking is highly correlated with days_from_account_created_until_first_booking and 1 other fieldsHigh correlation
days_from_account_created_until_first_booking is highly correlated with days_from_first_active_until_booking and 1 other fieldsHigh correlation
year_first_active is highly correlated with df_index and 1 other fieldsHigh correlation
month_first_active is highly correlated with weekodyear_first_active and 2 other fieldsHigh correlation
day_first_active is highly correlated with day_first_created_accountHigh correlation
dayofweek_first_active is highly correlated with dayofweek_first_created_accountHigh correlation
weekodyear_first_active is highly correlated with month_first_active and 2 other fieldsHigh correlation
year_first_booking is highly correlated with days_from_first_active_until_booking and 1 other fieldsHigh correlation
month_first_booking is highly correlated with weekofyear_first_bookingHigh correlation
weekofyear_first_booking is highly correlated with month_first_bookingHigh correlation
year_first_created_account is highly correlated with df_index and 1 other fieldsHigh correlation
month_first_created_account is highly correlated with month_first_active and 2 other fieldsHigh correlation
day_first_created_account is highly correlated with day_first_activeHigh correlation
dayofweek_first_created_account is highly correlated with dayofweek_first_activeHigh correlation
weekofyear_first_created_account is highly correlated with month_first_active and 2 other fieldsHigh correlation
days_from_first_active_until_account_created is highly skewed (γ1 = 69.29642597) Skewed
id is uniformly distributed Uniform
df_index has unique values Unique
id has unique values Unique
signup_flow has 162557 (78.7%) zeros Zeros
days_from_first_active_until_booking has 20738 (10.0%) zeros Zeros
days_from_first_active_until_account_created has 206421 (99.9%) zeros Zeros
days_from_account_created_until_first_booking has 20741 (10.0%) zeros Zeros
dayofweek_first_active has 31837 (15.4%) zeros Zeros
dayofweek_first_booking has 12407 (6.0%) zeros Zeros
dayofweek_first_created_account has 31830 (15.4%) zeros Zeros

Reproduction

Analysis started2021-05-18 16:45:02.541005
Analysis finished2021-05-18 16:46:33.435747
Duration1 minute and 30.89 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct206593
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108738.5086
Minimum0
Maximum213450
Zeros1
Zeros (%)< 0.1%
Memory size1.6 MiB
2021-05-18T13:46:33.666570image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13394.6
Q156479
median108694
Q3161529
95-th percentile203114.4
Maximum213450
Range213450
Interquartile range (IQR)105050

Descriptive statistics

Standard deviation60750.85989
Coefficient of variation (CV)0.558687632
Kurtosis-1.187821666
Mean108738.5086
Median Absolute Deviation (MAD)52523
Skewness-0.01019791288
Sum2.246461471 × 1010
Variance3690666977
MonotocityStrictly increasing
2021-05-18T13:46:33.843431image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
1774041
 
< 0.1%
1814901
 
< 0.1%
1835391
 
< 0.1%
1937801
 
< 0.1%
1958291
 
< 0.1%
1896861
 
< 0.1%
1917351
 
< 0.1%
1692081
 
< 0.1%
1712571
 
< 0.1%
Other values (206583)206583
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
ValueCountFrequency (%)
2134501
< 0.1%
2134491
< 0.1%
2134481
< 0.1%
2134471
< 0.1%
2134461
< 0.1%

id
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct206593
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
z29arvc6z1
 
1
hi3yh5nycd
 
1
h18n3790kq
 
1
qxhfd1mjtv
 
1
r4jgjzev0p
 
1
Other values (206588)
206588 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2065930
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique206593 ?
Unique (%)100.0%

Sample

1st rowgxn3p5htnn
2nd row820tgsjxq7
3rd row4ft3gnwmtx
4th rowbjjt8pjhuk
5th row87mebub9p4
ValueCountFrequency (%)
z29arvc6z11
 
< 0.1%
hi3yh5nycd1
 
< 0.1%
h18n3790kq1
 
< 0.1%
qxhfd1mjtv1
 
< 0.1%
r4jgjzev0p1
 
< 0.1%
w3h99953v71
 
< 0.1%
mab67ymkb61
 
< 0.1%
ex4tatbiqm1
 
< 0.1%
mz2ubfqbq91
 
< 0.1%
bjox76fv881
 
< 0.1%
Other values (206583)206583
> 99.9%
2021-05-18T13:46:34.843127image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
z29arvc6z11
 
< 0.1%
hi3yh5nycd1
 
< 0.1%
h18n3790kq1
 
< 0.1%
qxhfd1mjtv1
 
< 0.1%
r4jgjzev0p1
 
< 0.1%
w3h99953v71
 
< 0.1%
mab67ymkb61
 
< 0.1%
ex4tatbiqm1
 
< 0.1%
mz2ubfqbq91
 
< 0.1%
bjox76fv881
 
< 0.1%
Other values (206583)206583
> 99.9%

Most occurring characters

ValueCountFrequency (%)
t57855
 
2.8%
h57719
 
2.8%
y57712
 
2.8%
o57682
 
2.8%
f57593
 
2.8%
457586
 
2.8%
157560
 
2.8%
b57558
 
2.8%
j57550
 
2.8%
i57525
 
2.8%
Other values (26)1489590
72.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1492613
72.2%
Decimal Number573317
 
27.8%

Most frequent character per category

ValueCountFrequency (%)
t57855
 
3.9%
h57719
 
3.9%
y57712
 
3.9%
o57682
 
3.9%
f57593
 
3.9%
b57558
 
3.9%
j57550
 
3.9%
i57525
 
3.9%
w57514
 
3.9%
a57500
 
3.9%
Other values (16)916405
61.4%
ValueCountFrequency (%)
457586
10.0%
157560
10.0%
257493
10.0%
757460
10.0%
357364
10.0%
857349
10.0%
957336
10.0%
057106
10.0%
557052
10.0%
657011
9.9%

Most occurring scripts

ValueCountFrequency (%)
Latin1492613
72.2%
Common573317
 
27.8%

Most frequent character per script

ValueCountFrequency (%)
t57855
 
3.9%
h57719
 
3.9%
y57712
 
3.9%
o57682
 
3.9%
f57593
 
3.9%
b57558
 
3.9%
j57550
 
3.9%
i57525
 
3.9%
w57514
 
3.9%
a57500
 
3.9%
Other values (16)916405
61.4%
ValueCountFrequency (%)
457586
10.0%
157560
10.0%
257493
10.0%
757460
10.0%
357364
10.0%
857349
10.0%
957336
10.0%
057106
10.0%
557052
10.0%
657011
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2065930
100.0%

Most frequent character per block

ValueCountFrequency (%)
t57855
 
2.8%
h57719
 
2.8%
y57712
 
2.8%
o57682
 
2.8%
f57593
 
2.8%
457586
 
2.8%
157560
 
2.8%
b57558
 
2.8%
j57550
 
2.8%
i57525
 
2.8%
Other values (26)1489590
72.1%

gender
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
-unknown-
91706 
FEMALE
61520 
MALE
53092 
OTHER
 
275

Length

Max length9
Median length6
Mean length6.816382936
Min length4

Characters and Unicode

Total characters1408217
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-unknown-
2nd rowMALE
3rd rowFEMALE
4th rowFEMALE
5th row-unknown-
ValueCountFrequency (%)
-unknown-91706
44.4%
FEMALE61520
29.8%
MALE53092
25.7%
OTHER275
 
0.1%
2021-05-18T13:46:35.124067image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-18T13:46:35.202032image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
unknown91706
44.4%
female61520
29.8%
male53092
25.7%
other275
 
0.1%

Most occurring characters

ValueCountFrequency (%)
n275118
19.5%
-183412
13.0%
E176407
12.5%
M114612
8.1%
A114612
8.1%
L114612
8.1%
u91706
 
6.5%
k91706
 
6.5%
o91706
 
6.5%
w91706
 
6.5%
Other values (5)62620
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter641942
45.6%
Uppercase Letter582863
41.4%
Dash Punctuation183412
 
13.0%

Most frequent character per category

ValueCountFrequency (%)
E176407
30.3%
M114612
19.7%
A114612
19.7%
L114612
19.7%
F61520
 
10.6%
O275
 
< 0.1%
T275
 
< 0.1%
H275
 
< 0.1%
R275
 
< 0.1%
ValueCountFrequency (%)
n275118
42.9%
u91706
 
14.3%
k91706
 
14.3%
o91706
 
14.3%
w91706
 
14.3%
ValueCountFrequency (%)
-183412
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1224805
87.0%
Common183412
 
13.0%

Most frequent character per script

ValueCountFrequency (%)
n275118
22.5%
E176407
14.4%
M114612
9.4%
A114612
9.4%
L114612
9.4%
u91706
 
7.5%
k91706
 
7.5%
o91706
 
7.5%
w91706
 
7.5%
F61520
 
5.0%
Other values (4)1100
 
0.1%
ValueCountFrequency (%)
-183412
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1408217
100.0%

Most frequent character per block

ValueCountFrequency (%)
n275118
19.5%
-183412
13.0%
E176407
12.5%
M114612
8.1%
A114612
8.1%
L114612
8.1%
u91706
 
6.5%
k91706
 
6.5%
o91706
 
6.5%
w91706
 
6.5%
Other values (5)62620
 
4.4%

age
Real number (ℝ≥0)

Distinct99
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.11742411
Minimum16
Maximum115
Zeros0
Zeros (%)0.0%
Memory size1.6 MiB
2021-05-18T13:46:35.312150image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile24
Q132
median49
Q349
95-th percentile57
Maximum115
Range99
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.156497
Coefficient of variation (CV)0.28863344
Kurtosis4.626116599
Mean42.11742411
Median Absolute Deviation (MAD)6
Skewness1.001974223
Sum8701165
Variance147.7804194
MonotocityNot monotonic
2021-05-18T13:46:35.449406image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4985257
41.3%
306039
 
2.9%
315935
 
2.9%
295894
 
2.9%
285862
 
2.8%
325763
 
2.8%
275671
 
2.7%
335455
 
2.6%
264960
 
2.4%
344940
 
2.4%
Other values (89)70817
34.3%
ValueCountFrequency (%)
1626
 
< 0.1%
1764
 
< 0.1%
18665
0.3%
191097
0.5%
20533
0.3%
ValueCountFrequency (%)
11512
 
< 0.1%
1134
 
< 0.1%
1121
 
< 0.1%
1112
 
< 0.1%
110188
0.1%

signup_method
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
basic
147635 
facebook
58412 
google
 
546

Length

Max length8
Median length5
Mean length5.850861355
Min length5

Characters and Unicode

Total characters1208747
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfacebook
2nd rowfacebook
3rd rowbasic
4th rowfacebook
5th rowbasic
ValueCountFrequency (%)
basic147635
71.5%
facebook58412
 
28.3%
google546
 
0.3%
2021-05-18T13:46:35.754234image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-18T13:46:35.829075image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
basic147635
71.5%
facebook58412
 
28.3%
google546
 
0.3%

Most occurring characters

ValueCountFrequency (%)
a206047
17.0%
c206047
17.0%
b206047
17.0%
s147635
12.2%
i147635
12.2%
o117916
9.8%
e58958
 
4.9%
f58412
 
4.8%
k58412
 
4.8%
g1092
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1208747
100.0%

Most frequent character per category

ValueCountFrequency (%)
a206047
17.0%
c206047
17.0%
b206047
17.0%
s147635
12.2%
i147635
12.2%
o117916
9.8%
e58958
 
4.9%
f58412
 
4.8%
k58412
 
4.8%
g1092
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin1208747
100.0%

Most frequent character per script

ValueCountFrequency (%)
a206047
17.0%
c206047
17.0%
b206047
17.0%
s147635
12.2%
i147635
12.2%
o117916
9.8%
e58958
 
4.9%
f58412
 
4.8%
k58412
 
4.8%
g1092
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1208747
100.0%

Most frequent character per block

ValueCountFrequency (%)
a206047
17.0%
c206047
17.0%
b206047
17.0%
s147635
12.2%
i147635
12.2%
o117916
9.8%
e58958
 
4.9%
f58412
 
4.8%
k58412
 
4.8%
g1092
 
0.1%

signup_flow
Real number (ℝ≥0)

ZEROS

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.156946266
Minimum0
Maximum25
Zeros162557
Zeros (%)78.7%
Memory size1.6 MiB
2021-05-18T13:46:35.905160image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile25
Maximum25
Range25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.550683626
Coefficient of variation (CV)2.39176818
Kurtosis3.551615681
Mean3.156946266
Median Absolute Deviation (MAD)0
Skewness2.283783603
Sum652203
Variance57.01282322
MonotocityNot monotonic
2021-05-18T13:46:36.035986image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0162557
78.7%
2513724
 
6.6%
128897
 
4.3%
37550
 
3.7%
25522
 
2.7%
243975
 
1.9%
232793
 
1.4%
1837
 
0.4%
6240
 
0.1%
8237
 
0.1%
Other values (7)261
 
0.1%
ValueCountFrequency (%)
0162557
78.7%
1837
 
0.4%
25522
 
2.7%
37550
 
3.7%
41
 
< 0.1%
ValueCountFrequency (%)
2513724
6.6%
243975
 
1.9%
232793
 
1.4%
21195
 
0.1%
2014
 
< 0.1%

language
Categorical

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
en
199636 
zh
 
1599
fr
 
1146
es
 
888
ko
 
720
Other values (20)
 
2604

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters413186
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowen
2nd rowen
3rd rowen
4th rowen
5th rowen
ValueCountFrequency (%)
en199636
96.6%
zh1599
 
0.8%
fr1146
 
0.6%
es888
 
0.4%
ko720
 
0.3%
de715
 
0.3%
it489
 
0.2%
ru378
 
0.2%
pt234
 
0.1%
ja224
 
0.1%
Other values (15)564
 
0.3%
2021-05-18T13:46:36.349047image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
en199636
96.6%
zh1599
 
0.8%
fr1146
 
0.6%
es888
 
0.4%
ko720
 
0.3%
de715
 
0.3%
it489
 
0.2%
ru378
 
0.2%
pt234
 
0.1%
ja224
 
0.1%
Other values (15)564
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e201263
48.7%
n199760
48.3%
h1641
 
0.4%
z1599
 
0.4%
r1589
 
0.4%
f1160
 
0.3%
s1046
 
0.3%
t809
 
0.2%
d795
 
0.2%
o750
 
0.2%
Other values (9)2774
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter413186
100.0%

Most frequent character per category

ValueCountFrequency (%)
e201263
48.7%
n199760
48.3%
h1641
 
0.4%
z1599
 
0.4%
r1589
 
0.4%
f1160
 
0.3%
s1046
 
0.3%
t809
 
0.2%
d795
 
0.2%
o750
 
0.2%
Other values (9)2774
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Latin413186
100.0%

Most frequent character per script

ValueCountFrequency (%)
e201263
48.7%
n199760
48.3%
h1641
 
0.4%
z1599
 
0.4%
r1589
 
0.4%
f1160
 
0.3%
s1046
 
0.3%
t809
 
0.2%
d795
 
0.2%
o750
 
0.2%
Other values (9)2774
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII413186
100.0%

Most frequent character per block

ValueCountFrequency (%)
e201263
48.7%
n199760
48.3%
h1641
 
0.4%
z1599
 
0.4%
r1589
 
0.4%
f1160
 
0.3%
s1046
 
0.3%
t809
 
0.2%
d795
 
0.2%
o750
 
0.2%
Other values (9)2774
 
0.7%
Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
direct
133678 
sem-brand
25681 
sem-non-brand
17949 
seo
 
8420
other
 
8296
Other values (3)
 
12569

Length

Max length13
Median length6
Mean length6.7501077
Min length3

Characters and Unicode

Total characters1394525
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdirect
2nd rowseo
3rd rowdirect
4th rowdirect
5th rowdirect
ValueCountFrequency (%)
direct133678
64.7%
sem-brand25681
 
12.4%
sem-non-brand17949
 
8.7%
seo8420
 
4.1%
other8296
 
4.0%
api7736
 
3.7%
content3780
 
1.8%
remarketing1053
 
0.5%
2021-05-18T13:46:36.564866image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-18T13:46:36.640183image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
direct133678
64.7%
sem-brand25681
 
12.4%
sem-non-brand17949
 
8.7%
seo8420
 
4.1%
other8296
 
4.0%
api7736
 
3.7%
content3780
 
1.8%
remarketing1053
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e199910
14.3%
r187710
13.5%
d177308
12.7%
t150587
10.8%
i142467
10.2%
c137458
9.9%
n88141
6.3%
-61579
 
4.4%
a52419
 
3.8%
s52050
 
3.7%
Other values (7)144896
10.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1332946
95.6%
Dash Punctuation61579
 
4.4%

Most frequent character per category

ValueCountFrequency (%)
e199910
15.0%
r187710
14.1%
d177308
13.3%
t150587
11.3%
i142467
10.7%
c137458
10.3%
n88141
6.6%
a52419
 
3.9%
s52050
 
3.9%
m44683
 
3.4%
Other values (6)100213
7.5%
ValueCountFrequency (%)
-61579
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1332946
95.6%
Common61579
 
4.4%

Most frequent character per script

ValueCountFrequency (%)
e199910
15.0%
r187710
14.1%
d177308
13.3%
t150587
11.3%
i142467
10.7%
c137458
10.3%
n88141
6.6%
a52419
 
3.9%
s52050
 
3.9%
m44683
 
3.4%
Other values (6)100213
7.5%
ValueCountFrequency (%)
-61579
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1394525
100.0%

Most frequent character per block

ValueCountFrequency (%)
e199910
14.3%
r187710
13.5%
d177308
12.7%
t150587
10.8%
i142467
10.2%
c137458
9.9%
n88141
6.3%
-61579
 
4.4%
a52419
 
3.8%
s52050
 
3.7%
Other values (7)144896
10.4%
Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
direct
133438 
google
50252 
other
 
11867
craigslist
 
2964
bing
 
2253
Other values (13)
 
5819

Length

Max length19
Median length6
Mean length6.035233527
Min length3

Characters and Unicode

Total characters1246837
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowdirect
2nd rowgoogle
3rd rowdirect
4th rowdirect
5th rowdirect
ValueCountFrequency (%)
direct133438
64.6%
google50252
 
24.3%
other11867
 
5.7%
craigslist2964
 
1.4%
bing2253
 
1.1%
facebook2196
 
1.1%
padmapper766
 
0.4%
vast748
 
0.4%
facebook-open-graph545
 
0.3%
yahoo495
 
0.2%
Other values (8)1069
 
0.5%
2021-05-18T13:46:36.876079image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
direct133438
64.6%
google50252
 
24.3%
other11867
 
5.7%
craigslist2964
 
1.4%
bing2253
 
1.1%
facebook2196
 
1.1%
padmapper766
 
0.4%
vast748
 
0.4%
facebook-open-graph545
 
0.3%
yahoo495
 
0.2%
Other values (8)1069
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e200698
16.1%
r149795
12.0%
t149527
12.0%
i141974
11.4%
c139143
11.2%
d134251
10.8%
o119388
9.6%
g106881
8.6%
l53379
 
4.3%
h12907
 
1.0%
Other values (14)38894
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1245584
99.9%
Dash Punctuation1253
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
e200698
16.1%
r149795
12.0%
t149527
12.0%
i141974
11.4%
c139143
11.2%
d134251
10.8%
o119388
9.6%
g106881
8.6%
l53379
 
4.3%
h12907
 
1.0%
Other values (13)37641
 
3.0%
ValueCountFrequency (%)
-1253
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1245584
99.9%
Common1253
 
0.1%

Most frequent character per script

ValueCountFrequency (%)
e200698
16.1%
r149795
12.0%
t149527
12.0%
i141974
11.4%
c139143
11.2%
d134251
10.8%
o119388
9.6%
g106881
8.6%
l53379
 
4.3%
h12907
 
1.0%
Other values (13)37641
 
3.0%
ValueCountFrequency (%)
-1253
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1246837
100.0%

Most frequent character per block

ValueCountFrequency (%)
e200698
16.1%
r149795
12.0%
t149527
12.0%
i141974
11.4%
c139143
11.2%
d134251
10.8%
o119388
9.6%
g106881
8.6%
l53379
 
4.3%
h12907
 
1.0%
Other values (14)38894
 
3.1%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
untracked
108838 
linked
46084 
omg
43830 
tracked-other
 
6123
product
 
1545
Other values (2)
 
173

Length

Max length13
Median length9
Mean length7.161457552
Min length3

Characters and Unicode

Total characters1479507
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowuntracked
2nd rowuntracked
3rd rowuntracked
4th rowuntracked
5th rowuntracked
ValueCountFrequency (%)
untracked108838
52.7%
linked46084
22.3%
omg43830
21.2%
tracked-other6123
 
3.0%
product1545
 
0.7%
marketing139
 
0.1%
local ops34
 
< 0.1%
2021-05-18T13:46:37.106523image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-18T13:46:37.181512image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
untracked108838
52.7%
linked46084
22.3%
omg43830
21.2%
tracked-other6123
 
3.0%
product1545
 
0.7%
marketing139
 
0.1%
ops34
 
< 0.1%
local34
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e167307
11.3%
d162590
11.0%
k161184
10.9%
n155061
10.5%
t122768
8.3%
r122768
8.3%
c116540
7.9%
a115134
7.8%
u110383
7.5%
o51566
 
3.5%
Other values (9)194206
13.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1473350
99.6%
Dash Punctuation6123
 
0.4%
Space Separator34
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e167307
11.4%
d162590
11.0%
k161184
10.9%
n155061
10.5%
t122768
8.3%
r122768
8.3%
c116540
7.9%
a115134
7.8%
u110383
7.5%
o51566
 
3.5%
Other values (7)188049
12.8%
ValueCountFrequency (%)
-6123
100.0%
ValueCountFrequency (%)
34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1473350
99.6%
Common6157
 
0.4%

Most frequent character per script

ValueCountFrequency (%)
e167307
11.4%
d162590
11.0%
k161184
10.9%
n155061
10.5%
t122768
8.3%
r122768
8.3%
c116540
7.9%
a115134
7.8%
u110383
7.5%
o51566
 
3.5%
Other values (7)188049
12.8%
ValueCountFrequency (%)
-6123
99.4%
34
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1479507
100.0%

Most frequent character per block

ValueCountFrequency (%)
e167307
11.3%
d162590
11.0%
k161184
10.9%
n155061
10.5%
t122768
8.3%
r122768
8.3%
c116540
7.9%
a115134
7.8%
u110383
7.5%
o51566
 
3.5%
Other values (9)194206
13.1%

signup_app
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Web
177591 
iOS
17852 
Moweb
 
5771
Android
 
5379

Length

Max length7
Median length3
Mean length3.160015102
Min length3

Characters and Unicode

Total characters652837
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWeb
2nd rowWeb
3rd rowWeb
4th rowWeb
5th rowWeb
ValueCountFrequency (%)
Web177591
86.0%
iOS17852
 
8.6%
Moweb5771
 
2.8%
Android5379
 
2.6%
2021-05-18T13:46:37.395156image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-18T13:46:37.474508image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
web177591
86.0%
ios17852
 
8.6%
moweb5771
 
2.8%
android5379
 
2.6%

Most occurring characters

ValueCountFrequency (%)
e183362
28.1%
b183362
28.1%
W177591
27.2%
i23231
 
3.6%
O17852
 
2.7%
S17852
 
2.7%
o11150
 
1.7%
d10758
 
1.6%
M5771
 
0.9%
w5771
 
0.9%
Other values (3)16137
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter428392
65.6%
Uppercase Letter224445
34.4%

Most frequent character per category

ValueCountFrequency (%)
e183362
42.8%
b183362
42.8%
i23231
 
5.4%
o11150
 
2.6%
d10758
 
2.5%
w5771
 
1.3%
n5379
 
1.3%
r5379
 
1.3%
ValueCountFrequency (%)
W177591
79.1%
O17852
 
8.0%
S17852
 
8.0%
M5771
 
2.6%
A5379
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Latin652837
100.0%

Most frequent character per script

ValueCountFrequency (%)
e183362
28.1%
b183362
28.1%
W177591
27.2%
i23231
 
3.6%
O17852
 
2.7%
S17852
 
2.7%
o11150
 
1.7%
d10758
 
1.6%
M5771
 
0.9%
w5771
 
0.9%
Other values (3)16137
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII652837
100.0%

Most frequent character per block

ValueCountFrequency (%)
e183362
28.1%
b183362
28.1%
W177591
27.2%
i23231
 
3.6%
O17852
 
2.7%
S17852
 
2.7%
o11150
 
1.7%
d10758
 
1.6%
M5771
 
0.9%
w5771
 
0.9%
Other values (3)16137
 
2.5%
Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Mac Desktop
89255 
Windows Desktop
72410 
iPhone
20712 
iPad
14281 
Other/Unknown
 
4591
Other values (4)
 
5344

Length

Max length18
Median length11
Mean length11.53261727
Min length4

Characters and Unicode

Total characters2382558
Distinct characters30
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMac Desktop
2nd rowMac Desktop
3rd rowWindows Desktop
4th rowMac Desktop
5th rowMac Desktop
ValueCountFrequency (%)
Mac Desktop89255
43.2%
Windows Desktop72410
35.0%
iPhone20712
 
10.0%
iPad14281
 
6.9%
Other/Unknown4591
 
2.2%
Android Phone2788
 
1.3%
Android Tablet1285
 
0.6%
Desktop (Other)1196
 
0.6%
SmartPhone (Other)75
 
< 0.1%
2021-05-18T13:46:37.652332image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-18T13:46:37.732953image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
desktop162861
43.6%
mac89255
23.9%
windows72410
19.4%
iphone20712
 
5.5%
ipad14281
 
3.8%
other/unknown4591
 
1.2%
android4073
 
1.1%
phone2788
 
0.7%
tablet1285
 
0.3%
other1271
 
0.3%

Most occurring characters

ValueCountFrequency (%)
o267510
11.2%
s235271
 
9.9%
e193583
 
8.1%
t170083
 
7.1%
k167452
 
7.0%
167009
 
7.0%
D162861
 
6.8%
p162861
 
6.8%
n113831
 
4.8%
i111476
 
4.7%
Other values (20)630621
26.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1830148
76.8%
Uppercase Letter378268
 
15.9%
Space Separator167009
 
7.0%
Other Punctuation4591
 
0.2%
Open Punctuation1271
 
0.1%
Close Punctuation1271
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
o267510
14.6%
s235271
12.9%
e193583
10.6%
t170083
9.3%
k167452
9.1%
p162861
8.9%
n113831
6.2%
i111476
6.1%
a104896
 
5.7%
d94837
 
5.2%
Other values (7)208348
11.4%
ValueCountFrequency (%)
D162861
43.1%
M89255
23.6%
W72410
19.1%
P37856
 
10.0%
O5862
 
1.5%
U4591
 
1.2%
A4073
 
1.1%
T1285
 
0.3%
S75
 
< 0.1%
ValueCountFrequency (%)
167009
100.0%
ValueCountFrequency (%)
/4591
100.0%
ValueCountFrequency (%)
(1271
100.0%
ValueCountFrequency (%)
)1271
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2208416
92.7%
Common174142
 
7.3%

Most frequent character per script

ValueCountFrequency (%)
o267510
12.1%
s235271
10.7%
e193583
 
8.8%
t170083
 
7.7%
k167452
 
7.6%
D162861
 
7.4%
p162861
 
7.4%
n113831
 
5.2%
i111476
 
5.0%
a104896
 
4.7%
Other values (16)518592
23.5%
ValueCountFrequency (%)
167009
95.9%
/4591
 
2.6%
(1271
 
0.7%
)1271
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2382558
100.0%

Most frequent character per block

ValueCountFrequency (%)
o267510
11.2%
s235271
 
9.9%
e193583
 
8.1%
t170083
 
7.1%
k167452
 
7.0%
167009
 
7.0%
D162861
 
6.8%
p162861
 
6.8%
n113831
 
4.8%
i111476
 
4.7%
Other values (20)630621
26.5%

first_browser
Categorical

HIGH CARDINALITY

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Chrome
63620 
Safari
44981 
Firefox
33513 
-unknown-
21166 
IE
20970 
Other values (47)
22343 

Length

Max length20
Median length6
Mean length6.808502708
Min length2

Characters and Unicode

Total characters1406589
Distinct characters50
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st rowChrome
2nd rowChrome
3rd rowIE
4th rowFirefox
5th rowChrome
ValueCountFrequency (%)
Chrome63620
30.8%
Safari44981
21.8%
Firefox33513
16.2%
-unknown-21166
 
10.2%
IE20970
 
10.2%
Mobile Safari19195
 
9.3%
Chrome Mobile1258
 
0.6%
Android Browser844
 
0.4%
AOL Explorer240
 
0.1%
Opera187
 
0.1%
Other values (42)619
 
0.3%
2021-05-18T13:46:37.995286image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chrome64878
28.4%
safari64176
28.1%
firefox33543
14.7%
unknown21166
 
9.3%
ie21006
 
9.2%
mobile20521
 
9.0%
browser907
 
0.4%
android844
 
0.4%
explorer273
 
0.1%
aol240
 
0.1%
Other values (48)799
 
0.3%

Most occurring characters

ValueCountFrequency (%)
r166259
11.8%
o142522
 
10.1%
a128752
 
9.2%
e120588
 
8.6%
i119405
 
8.5%
f97719
 
6.9%
m65051
 
4.6%
h65000
 
4.6%
C64982
 
4.6%
n64472
 
4.6%
Other values (40)371839
26.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1113623
79.2%
Uppercase Letter228859
 
16.3%
Dash Punctuation42332
 
3.0%
Space Separator21760
 
1.5%
Other Punctuation11
 
< 0.1%
Decimal Number4
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
r166259
14.9%
o142522
12.8%
a128752
11.6%
e120588
10.8%
i119405
10.7%
f97719
8.8%
m65051
 
5.8%
h65000
 
5.8%
n64472
 
5.8%
x33881
 
3.0%
Other values (14)109974
9.9%
ValueCountFrequency (%)
C64982
28.4%
S64378
28.1%
F33561
14.7%
E21281
 
9.3%
I21037
 
9.2%
M20657
 
9.0%
A1125
 
0.5%
B1039
 
0.5%
O442
 
0.2%
L240
 
0.1%
Other values (10)117
 
0.1%
ValueCountFrequency (%)
02
50.0%
21
25.0%
71
25.0%
ValueCountFrequency (%)
-42332
100.0%
ValueCountFrequency (%)
21760
100.0%
ValueCountFrequency (%)
.11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1342482
95.4%
Common64107
 
4.6%

Most frequent character per script

ValueCountFrequency (%)
r166259
12.4%
o142522
10.6%
a128752
9.6%
e120588
 
9.0%
i119405
 
8.9%
f97719
 
7.3%
m65051
 
4.8%
h65000
 
4.8%
C64982
 
4.8%
n64472
 
4.8%
Other values (34)307732
22.9%
ValueCountFrequency (%)
-42332
66.0%
21760
33.9%
.11
 
< 0.1%
02
 
< 0.1%
21
 
< 0.1%
71
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1406589
100.0%

Most frequent character per block

ValueCountFrequency (%)
r166259
11.8%
o142522
 
10.1%
a128752
 
9.2%
e120588
 
8.6%
i119405
 
8.5%
f97719
 
6.9%
m65051
 
4.6%
h65000
 
4.6%
C64982
 
4.6%
n64472
 
4.6%
Other values (40)371839
26.4%
Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
NDF
119810 
US
60800 
other
 
9935
FR
 
4881
IT
 
2776
Other values (7)
 
8391

Length

Max length5
Median length3
Mean length2.724201691
Min length2

Characters and Unicode

Total characters562801
Distinct characters20
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNDF
2nd rowNDF
3rd rowUS
4th rowother
5th rowUS
ValueCountFrequency (%)
NDF119810
58.0%
US60800
29.4%
other9935
 
4.8%
FR4881
 
2.4%
IT2776
 
1.3%
GB2285
 
1.1%
ES2203
 
1.1%
CA1385
 
0.7%
DE1033
 
0.5%
NL746
 
0.4%
Other values (2)739
 
0.4%
2021-05-18T13:46:38.233370image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ndf119810
58.0%
us60800
29.4%
other9935
 
4.8%
fr4881
 
2.4%
it2776
 
1.3%
gb2285
 
1.1%
es2203
 
1.1%
ca1385
 
0.7%
de1033
 
0.5%
nl746
 
0.4%
Other values (2)739
 
0.4%

Most occurring characters

ValueCountFrequency (%)
F124691
22.2%
D120843
21.5%
N120556
21.4%
S63003
11.2%
U61326
10.9%
o9935
 
1.8%
t9935
 
1.8%
h9935
 
1.8%
e9935
 
1.8%
r9935
 
1.8%
Other values (10)22707
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter513126
91.2%
Lowercase Letter49675
 
8.8%

Most frequent character per category

ValueCountFrequency (%)
F124691
24.3%
D120843
23.6%
N120556
23.5%
S63003
12.3%
U61326
12.0%
R4881
 
1.0%
E3236
 
0.6%
T2989
 
0.6%
I2776
 
0.5%
G2285
 
0.4%
Other values (5)6540
 
1.3%
ValueCountFrequency (%)
o9935
20.0%
t9935
20.0%
h9935
20.0%
e9935
20.0%
r9935
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin562801
100.0%

Most frequent character per script

ValueCountFrequency (%)
F124691
22.2%
D120843
21.5%
N120556
21.4%
S63003
11.2%
U61326
10.9%
o9935
 
1.8%
t9935
 
1.8%
h9935
 
1.8%
e9935
 
1.8%
r9935
 
1.8%
Other values (10)22707
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII562801
100.0%

Most frequent character per block

ValueCountFrequency (%)
F124691
22.2%
D120843
21.5%
N120556
21.4%
S63003
11.2%
U61326
10.9%
o9935
 
1.8%
t9935
 
1.8%
h9935
 
1.8%
e9935
 
1.8%
r9935
 
1.8%
Other values (10)22707
 
4.0%

days_from_first_active_until_booking
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1976
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3379.391916
Minimum0
Maximum7393
Zeros20738
Zeros (%)10.0%
Memory size1.6 MiB
2021-05-18T13:46:38.362334image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median5516
Q35778
95-th percentile6208
Maximum7393
Range7393
Interquartile range (IQR)5772

Descriptive statistics

Standard deviation2845.881127
Coefficient of variation (CV)0.8421281692
Kurtosis-1.878777468
Mean3379.391916
Median Absolute Deviation (MAD)618
Skewness-0.307731876
Sum698158714
Variance8099039.388
MonotocityNot monotonic
2021-05-18T13:46:38.503228image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
020738
 
10.0%
114288
 
6.9%
26307
 
3.1%
33894
 
1.9%
42845
 
1.4%
52193
 
1.1%
61735
 
0.8%
71611
 
0.8%
81275
 
0.6%
91024
 
0.5%
Other values (1966)150683
72.9%
ValueCountFrequency (%)
020738
10.0%
114288
6.9%
26307
 
3.1%
33894
 
1.9%
42845
 
1.4%
ValueCountFrequency (%)
73931
< 0.1%
73281
< 0.1%
71012
< 0.1%
70991
< 0.1%
70952
< 0.1%

days_from_first_active_until_account_created
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct142
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.236682753
Minimum0
Maximum1456
Zeros206421
Zeros (%)99.9%
Memory size1.6 MiB
2021-05-18T13:46:38.648683image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1456
Range1456
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.1122496
Coefficient of variation (CV)51.17504104
Kurtosis5699.565643
Mean0.236682753
Median Absolute Deviation (MAD)0
Skewness69.29642597
Sum48897
Variance146.7065904
MonotocityNot monotonic
2021-05-18T13:46:38.793605image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0206421
99.9%
16
 
< 0.1%
64
 
< 0.1%
33
 
< 0.1%
293
 
< 0.1%
53
 
< 0.1%
73
 
< 0.1%
23
 
< 0.1%
1762
 
< 0.1%
202
 
< 0.1%
Other values (132)143
 
0.1%
ValueCountFrequency (%)
0206421
99.9%
16
 
< 0.1%
23
 
< 0.1%
33
 
< 0.1%
42
 
< 0.1%
ValueCountFrequency (%)
14561
< 0.1%
13691
< 0.1%
13611
< 0.1%
11481
< 0.1%
10361
< 0.1%

days_from_account_created_until_first_booking
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct1965
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3379.155233
Minimum-349
Maximum7101
Zeros20741
Zeros (%)10.0%
Memory size1.6 MiB
2021-05-18T13:46:38.949661image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-349
5-th percentile0
Q16
median5516
Q35778
95-th percentile6208
Maximum7101
Range7450
Interquartile range (IQR)5772

Descriptive statistics

Standard deviation2845.939191
Coefficient of variation (CV)0.8422043366
Kurtosis-1.8788465
Mean3379.155233
Median Absolute Deviation (MAD)618
Skewness-0.3077348916
Sum698109817
Variance8099369.879
MonotocityNot monotonic
2021-05-18T13:46:39.095562image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
020741
 
10.0%
114289
 
6.9%
26309
 
3.1%
33897
 
1.9%
42845
 
1.4%
52197
 
1.1%
61738
 
0.8%
71611
 
0.8%
81276
 
0.6%
91022
 
0.5%
Other values (1955)150668
72.9%
ValueCountFrequency (%)
-3491
< 0.1%
-3471
< 0.1%
-3381
< 0.1%
-3081
< 0.1%
-2981
< 0.1%
ValueCountFrequency (%)
71012
< 0.1%
70991
< 0.1%
70952
< 0.1%
70941
< 0.1%
70921
< 0.1%

year_first_active
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.062703
Minimum2009
Maximum2014
Zeros0
Zeros (%)0.0%
Memory size1.6 MiB
2021-05-18T13:46:39.214079image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2011
Q12013
median2013
Q32014
95-th percentile2014
Maximum2014
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9011508238
Coefficient of variation (CV)0.0004476516417
Kurtosis0.3051540033
Mean2013.062703
Median Absolute Deviation (MAD)1
Skewness-0.8084367444
Sum415884663
Variance0.8120728072
MonotocityIncreasing
2021-05-18T13:46:39.316634image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
201381841
39.6%
201475496
36.5%
201237950
18.4%
20119331
 
4.5%
20101970
 
1.0%
20095
 
< 0.1%
ValueCountFrequency (%)
20095
 
< 0.1%
20101970
 
1.0%
20119331
 
4.5%
201237950
18.4%
201381841
39.6%
ValueCountFrequency (%)
201475496
36.5%
201381841
39.6%
201237950
18.4%
20119331
 
4.5%
20101970
 
1.0%

month_first_active
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.016956044
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size1.6 MiB
2021-05-18T13:46:39.419320image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.221454147
Coefficient of variation (CV)0.5353959915
Kurtosis-0.9528826524
Mean6.016956044
Median Absolute Deviation (MAD)3
Skewness0.2529472005
Sum1243061
Variance10.37776682
MonotocityNot monotonic
2021-05-18T13:46:39.514442image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
627033
13.1%
525525
12.4%
421333
10.3%
319482
9.4%
116768
8.1%
215853
7.7%
914774
7.2%
814061
6.8%
713410
6.5%
1013031
6.3%
Other values (2)25323
12.3%
ValueCountFrequency (%)
116768
8.1%
215853
7.7%
319482
9.4%
421333
10.3%
525525
12.4%
ValueCountFrequency (%)
1212799
6.2%
1112524
6.1%
1013031
6.3%
914774
7.2%
814061
6.8%

day_first_active
Real number (ℝ≥0)

HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.8726772
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Memory size1.6 MiB
2021-05-18T13:46:39.633263image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.74204279
Coefficient of variation (CV)0.5507604471
Kurtosis-1.187475222
Mean15.8726772
Median Absolute Deviation (MAD)8
Skewness-0.01122201687
Sum3279184
Variance76.42331214
MonotocityNot monotonic
2021-05-18T13:46:39.757497image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
247182
 
3.5%
207014
 
3.4%
187010
 
3.4%
166988
 
3.4%
236988
 
3.4%
196956
 
3.4%
286915
 
3.3%
266908
 
3.3%
176908
 
3.3%
136889
 
3.3%
Other values (21)136835
66.2%
ValueCountFrequency (%)
15967
2.9%
26561
3.2%
36750
3.3%
46620
3.2%
56817
3.3%
ValueCountFrequency (%)
313607
1.7%
306587
3.2%
296363
3.1%
286915
3.3%
276842
3.3%

dayofweek_first_active
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.762053893
Minimum0
Maximum6
Zeros31837
Zeros (%)15.4%
Memory size1.6 MiB
2021-05-18T13:46:39.865643image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.944268623
Coefficient of variation (CV)0.7039213201
Kurtosis-1.149772853
Mean2.762053893
Median Absolute Deviation (MAD)2
Skewness0.1677710956
Sum570621
Variance3.780180478
MonotocityNot monotonic
2021-05-18T13:46:39.958539image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
133988
16.5%
233041
16.0%
031837
15.4%
331504
15.2%
428807
13.9%
623731
11.5%
523685
11.5%
ValueCountFrequency (%)
031837
15.4%
133988
16.5%
233041
16.0%
331504
15.2%
428807
13.9%
ValueCountFrequency (%)
623731
11.5%
523685
11.5%
428807
13.9%
331504
15.2%
233041
16.0%

weekodyear_first_active
Real number (ℝ≥0)

HIGH CORRELATION

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.37388973
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Memory size1.6 MiB
2021-05-18T13:46:40.091258image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median23
Q336
95-th percentile49
Maximum53
Range52
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.95400971
Coefficient of variation (CV)0.5724982704
Kurtosis-0.9411483504
Mean24.37388973
Median Absolute Deviation (MAD)11
Skewness0.2534181308
Sum5035475
Variance194.714387
MonotocityNot monotonic
2021-05-18T13:46:40.244427image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
266779
 
3.3%
256426
 
3.1%
246164
 
3.0%
216116
 
3.0%
236062
 
2.9%
206057
 
2.9%
225602
 
2.7%
195490
 
2.7%
185433
 
2.6%
175309
 
2.6%
Other values (43)147155
71.2%
ValueCountFrequency (%)
13196
1.5%
23824
1.9%
34026
1.9%
43785
1.8%
53794
1.8%
ValueCountFrequency (%)
533
 
< 0.1%
522671
1.3%
512784
1.3%
502890
1.4%
493170
1.5%

year_first_booking
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2022.314619
Minimum2010
Maximum2029
Zeros0
Zeros (%)0.0%
Memory size1.6 MiB
2021-05-18T13:46:40.354732image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2012
Q12013
median2029
Q32029
95-th percentile2029
Maximum2029
Range19
Interquartile range (IQR)16

Descriptive statistics

Standard deviation7.880815138
Coefficient of variation (CV)0.003896928334
Kurtosis-1.852958414
Mean2022.314619
Median Absolute Deviation (MAD)0
Skewness-0.3441378278
Sum417796044
Variance62.10724723
MonotocityNot monotonic
2021-05-18T13:46:40.454682image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2029119810
58.0%
201432334
 
15.7%
201331083
 
15.0%
201215797
 
7.6%
20114690
 
2.3%
20151771
 
0.9%
20101108
 
0.5%
ValueCountFrequency (%)
20101108
 
0.5%
20114690
 
2.3%
201215797
7.6%
201331083
15.0%
201432334
15.7%
ValueCountFrequency (%)
2029119810
58.0%
20151771
 
0.9%
201432334
 
15.7%
201331083
 
15.0%
201215797
 
7.6%

month_first_booking
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.043592958
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size1.6 MiB
2021-05-18T13:46:40.570193image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median6
Q36
95-th percentile10
Maximum12
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.059181648
Coefficient of variation (CV)0.3407214321
Kurtosis1.874039975
Mean6.043592958
Median Absolute Deviation (MAD)0
Skewness0.385503438
Sum1248564
Variance4.240229059
MonotocityNot monotonic
2021-05-18T13:46:40.674710image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6130148
63.0%
510322
 
5.0%
48624
 
4.2%
38159
 
3.9%
77073
 
3.4%
86854
 
3.3%
26616
 
3.2%
96402
 
3.1%
16338
 
3.1%
106009
 
2.9%
Other values (2)10048
 
4.9%
ValueCountFrequency (%)
16338
3.1%
26616
3.2%
38159
3.9%
48624
4.2%
510322
5.0%
ValueCountFrequency (%)
124944
2.4%
115104
2.5%
106009
2.9%
96402
3.1%
86854
3.3%

day_first_booking
Real number (ℝ≥0)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.27325708
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Memory size1.6 MiB
2021-05-18T13:46:40.793269image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q115
median15
Q315
95-th percentile27
Maximum31
Range30
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.663609584
Coefficient of variation (CV)0.3708187163
Kurtosis1.351926905
Mean15.27325708
Median Absolute Deviation (MAD)0
Skewness0.2350356536
Sum3155348
Variance32.07647352
MonotocityNot monotonic
2021-05-18T13:46:40.918737image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
15122760
59.4%
103009
 
1.5%
172996
 
1.5%
112982
 
1.4%
162964
 
1.4%
132950
 
1.4%
52918
 
1.4%
122892
 
1.4%
82882
 
1.4%
32882
 
1.4%
Other values (21)57358
27.8%
ValueCountFrequency (%)
12690
1.3%
22807
1.4%
32882
1.4%
42784
1.3%
52918
1.4%
ValueCountFrequency (%)
311526
0.7%
302650
1.3%
292557
1.2%
282809
1.4%
272698
1.3%

dayofweek_first_booking
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.497320819
Minimum0
Maximum6
Zeros12407
Zeros (%)6.0%
Memory size1.6 MiB
2021-05-18T13:46:41.029920image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median4
Q34
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.374571803
Coefficient of variation (CV)0.3930356618
Kurtosis0.9424697317
Mean3.497320819
Median Absolute Deviation (MAD)0
Skewness-1.10941395
Sum722522
Variance1.889447641
MonotocityNot monotonic
2021-05-18T13:46:41.121285image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4132782
64.3%
214029
 
6.8%
113970
 
6.8%
313627
 
6.6%
012407
 
6.0%
510183
 
4.9%
69595
 
4.6%
ValueCountFrequency (%)
012407
 
6.0%
113970
 
6.8%
214029
 
6.8%
313627
 
6.6%
4132782
64.3%
ValueCountFrequency (%)
69595
 
4.6%
510183
 
4.9%
4132782
64.3%
313627
 
6.6%
214029
 
6.8%

weekofyear_first_booking
Real number (ℝ≥0)

HIGH CORRELATION

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.30690778
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Memory size1.6 MiB
2021-05-18T13:46:41.246936image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q124
median24
Q324
95-th percentile44
Maximum53
Range52
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.923814519
Coefficient of variation (CV)0.3671308008
Kurtosis1.914570671
Mean24.30690778
Median Absolute Deviation (MAD)0
Skewness0.4439373104
Sum5021637
Variance79.63446557
MonotocityNot monotonic
2021-05-18T13:46:41.413845image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24122272
59.2%
262488
 
1.2%
212451
 
1.2%
202434
 
1.2%
252403
 
1.2%
232366
 
1.1%
182277
 
1.1%
192263
 
1.1%
222218
 
1.1%
152035
 
1.0%
Other values (43)63386
30.7%
ValueCountFrequency (%)
11092
0.5%
21438
0.7%
31717
0.8%
41401
0.7%
51444
0.7%
ValueCountFrequency (%)
531
 
< 0.1%
52915
0.4%
511130
0.5%
501172
0.6%
491249
0.6%

year_first_created_account
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2013
81851 
2014
75532 
2012
37936 
2011
9313 
2010
 
1961

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters826372
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2010
2nd row2011
3rd row2010
4th row2011
5th row2010
ValueCountFrequency (%)
201381851
39.6%
201475532
36.6%
201237936
18.4%
20119313
 
4.5%
20101961
 
0.9%
2021-05-18T13:46:42.089219image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-18T13:46:42.161515image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
201381851
39.6%
201475532
36.6%
201237936
18.4%
20119313
 
4.5%
20101961
 
0.9%

Most occurring characters

ValueCountFrequency (%)
2244529
29.6%
1215906
26.1%
0208554
25.2%
381851
 
9.9%
475532
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number826372
100.0%

Most frequent character per category

ValueCountFrequency (%)
2244529
29.6%
1215906
26.1%
0208554
25.2%
381851
 
9.9%
475532
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Common826372
100.0%

Most frequent character per script

ValueCountFrequency (%)
2244529
29.6%
1215906
26.1%
0208554
25.2%
381851
 
9.9%
475532
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII826372
100.0%

Most frequent character per block

ValueCountFrequency (%)
2244529
29.6%
1215906
26.1%
0208554
25.2%
381851
 
9.9%
475532
 
9.1%

month_first_created_account
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.016994767
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size1.6 MiB
2021-05-18T13:46:42.249640image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.221653778
Coefficient of variation (CV)0.5354257238
Kurtosis-0.9530348208
Mean6.016994767
Median Absolute Deviation (MAD)3
Skewness0.252885905
Sum1243069
Variance10.37905307
MonotocityNot monotonic
2021-05-18T13:46:42.353786image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
627028
13.1%
525531
12.4%
421324
10.3%
319481
9.4%
116773
8.1%
215855
7.7%
914780
7.2%
814060
6.8%
713405
6.5%
1013030
6.3%
Other values (2)25326
12.3%
ValueCountFrequency (%)
116773
8.1%
215855
7.7%
319481
9.4%
421324
10.3%
525531
12.4%
ValueCountFrequency (%)
1212803
6.2%
1112523
6.1%
1013030
6.3%
914780
7.2%
814060
6.8%

day_first_created_account
Real number (ℝ≥0)

HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.8729531
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Memory size1.6 MiB
2021-05-18T13:46:42.469922image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.742507746
Coefficient of variation (CV)0.5507801661
Kurtosis-1.187560979
Mean15.8729531
Median Absolute Deviation (MAD)8
Skewness-0.01138779605
Sum3279241
Variance76.43144168
MonotocityNot monotonic
2021-05-18T13:46:42.593750image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
247183
 
3.5%
207018
 
3.4%
187009
 
3.4%
166994
 
3.4%
236990
 
3.4%
196955
 
3.4%
286921
 
3.4%
176904
 
3.3%
266904
 
3.3%
136883
 
3.3%
Other values (21)136832
66.2%
ValueCountFrequency (%)
15969
2.9%
26564
3.2%
36754
3.3%
46620
3.2%
56817
3.3%
ValueCountFrequency (%)
313605
1.7%
306588
3.2%
296366
3.1%
286921
3.4%
276841
3.3%

dayofweek_first_created_account
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.762199106
Minimum0
Maximum6
Zeros31830
Zeros (%)15.4%
Memory size1.6 MiB
2021-05-18T13:46:42.697170image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.944268962
Coefficient of variation (CV)0.7038844367
Kurtosis-1.1498462
Mean2.762199106
Median Absolute Deviation (MAD)2
Skewness0.1677017806
Sum570651
Variance3.780181796
MonotocityNot monotonic
2021-05-18T13:46:42.784176image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
133992
16.5%
233040
16.0%
031830
15.4%
331499
15.2%
428811
13.9%
623733
11.5%
523688
11.5%
ValueCountFrequency (%)
031830
15.4%
133992
16.5%
233040
16.0%
331499
15.2%
428811
13.9%
ValueCountFrequency (%)
623733
11.5%
523688
11.5%
428811
13.9%
331499
15.2%
233040
16.0%

weekofyear_first_created_account
Real number (ℝ≥0)

HIGH CORRELATION

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.37418015
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Memory size1.6 MiB
2021-05-18T13:46:42.900586image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median23
Q336
95-th percentile49
Maximum53
Range52
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.95489097
Coefficient of variation (CV)0.5725276042
Kurtosis-0.9413055266
Mean24.37418015
Median Absolute Deviation (MAD)11
Skewness0.2533569758
Sum5035535
Variance194.7389819
MonotocityNot monotonic
2021-05-18T13:46:43.040554image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
266777
 
3.3%
256426
 
3.1%
246164
 
3.0%
216116
 
3.0%
236061
 
2.9%
206057
 
2.9%
225606
 
2.7%
195486
 
2.7%
185441
 
2.6%
175308
 
2.6%
Other values (43)147151
71.2%
ValueCountFrequency (%)
13198
1.5%
23826
1.9%
34025
1.9%
43785
1.8%
53795
1.8%
ValueCountFrequency (%)
533
 
< 0.1%
522671
1.3%
512785
1.3%
502891
1.4%
493173
1.5%

Interactions

2021-05-18T13:45:32.712818image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:32.833866image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:32.952637image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:33.078511image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:33.211396image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:33.337856image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:33.475894image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:33.647081image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:33.802547image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:33.956184image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:34.130624image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:34.296955image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:34.451251image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:34.625372image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:34.793364image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:34.953228image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:35.126499image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:35.280993image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:35.450493image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:35.606381image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:35.743003image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:35.861064image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:35.984670image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:36.111561image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:36.235800image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:36.361209image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:36.476625image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:36.600583image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:36.784363image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:37.113915image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:37.299130image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:37.494572image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:37.661918image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:37.842551image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:37.999166image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:38.176676image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:38.374952image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:38.553373image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:38.726657image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:38.907243image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:39.081791image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:39.256530image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:39.405272image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:39.558252image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:39.692894image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:39.824963image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:39.971742image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:40.100794image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:40.238721image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:40.373833image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:40.501024image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:40.654260image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:40.790279image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:40.911722image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:41.034326image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:41.156769image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:41.285343image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:41.410285image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:41.541377image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:41.667361image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:41.800984image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:41.938107image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:42.076677image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:42.206933image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:42.348296image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:42.501849image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:42.650028image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:42.800817image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:42.946572image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:43.080241image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:43.352350image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:43.492869image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:43.624389image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:43.755821image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:43.889847image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:44.030135image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:44.162293image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:44.307678image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:44.492119image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:44.677437image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:44.851403image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:44.993735image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:45.125532image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:45.255882image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:45.390549image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:45.522391image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:45.653022image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:45.795259image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:45.957358image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:46.105558image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:46.254559image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:46.392532image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:46.526148image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:46.666630image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:46.836251image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:46.972055image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:47.105467image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:47.239076image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:47.423791image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:47.607164image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:47.780629image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:47.924291image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:48.085098image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:48.266295image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:48.408455image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:48.600054image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:48.788260image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:48.967298image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:49.138513image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:49.313906image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:45:49.467319image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-05-18T13:46:19.605233image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:19.732133image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:19.856030image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:19.980225image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:20.116349image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:20.268792image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:20.397594image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:20.524537image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:20.650566image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:20.775994image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:20.900439image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:21.034260image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:21.166959image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:21.291587image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:21.419875image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:21.553182image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:21.688800image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:21.821074image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:21.946083image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:22.071780image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:22.202029image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:22.334610image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:22.514355image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:22.706415image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:22.862132image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:23.045211image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:23.205328image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:23.362184image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:23.511584image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:23.674417image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:23.808977image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:23.988617image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:24.135910image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:24.271940image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:24.404989image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:24.546552image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:24.717713image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:24.844204image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:24.969283image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:25.514817image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:25.692723image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:25.861336image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:26.018183image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:26.147498image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:26.277859image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:26.416407image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:26.544686image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:26.671665image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:26.799761image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:26.925745image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:27.055214image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:27.182260image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:27.374521image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:27.542109image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:27.715375image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:27.882665image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:28.027707image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:28.150597image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:28.282882image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:28.411281image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:28.560033image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:28.764066image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:28.932697image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:29.104443image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:29.282748image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:29.448771image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:29.623899image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-18T13:46:29.800399image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-05-18T13:46:43.184632image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-18T13:46:43.453388image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-18T13:46:43.752996image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-18T13:46:44.053253image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-18T13:46:44.394392image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-05-18T13:46:30.591207image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-18T13:46:32.210877image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexidgenderagesignup_methodsignup_flowlanguageaffiliate_channelaffiliate_providerfirst_affiliate_trackedsignup_appfirst_device_typefirst_browsercountry_destinationdays_from_first_active_until_bookingdays_from_first_active_until_account_createddays_from_account_created_until_first_bookingyear_first_activemonth_first_activeday_first_activedayofweek_first_activeweekodyear_first_activeyear_first_bookingmonth_first_bookingday_first_bookingdayofweek_first_bookingweekofyear_first_bookingyear_first_created_accountmonth_first_created_accountday_first_created_accountdayofweek_first_created_accountweekofyear_first_created_account
00gxn3p5htnn-unknown-49facebook0endirectdirectuntrackedWebMac DesktopChromeNDF73934666927200931931220296154242010628026
11820tgsjxq7MALE38facebook0enseogoogleuntrackedWebMac DesktopChromeNDF73287326596200952352120296154242011525221
224ft3gnwmtxFEMALE56basic3endirectdirectuntrackedWebWindows DesktopIEUS419476-572009691242010820312010928139
33bjjt8pjhukFEMALE42facebook0endirectdirectuntrackedWebMac DesktopFirefoxother1043765278200910315442012985362011125049
4487mebub9p4-unknown-41basic0endirectdirectuntrackedWebMac DesktopChromeUS72280-20820091281502010218372010914137
55osr2jwljor-unknown-49basic0enotherotheromgWebMac DesktopChromeUS101201011453201012553201011453
66lsw9q7uk0jFEMALE46basic0enothercraigslistuntrackedWebMac DesktopSafariUS30320101255320101511201012553
770d01nltbrsFEMALE47basic0endirectdirectomgWebMac DesktopSafariUS10010201013653201011322201013653
88a1vcnhxeijFEMALE50basic0enothercraigslistuntrackedWebMac DesktopSafariUS206020620101401201072933020101401
996uh8zyj2gn-unknown-46basic0enothercraigslistomgWebMac DesktopFirefoxUS000201014012010140120101401

Last rows

df_indexidgenderagesignup_methodsignup_flowlanguageaffiliate_channelaffiliate_providerfirst_affiliate_trackedsignup_appfirst_device_typefirst_browsercountry_destinationdays_from_first_active_until_bookingdays_from_first_active_until_account_createddays_from_account_created_until_first_bookingyear_first_activemonth_first_activeday_first_activedayofweek_first_activeweekodyear_first_activeyear_first_bookingmonth_first_bookingday_first_bookingdayofweek_first_bookingweekofyear_first_bookingyear_first_created_accountmonth_first_created_accountday_first_created_accountdayofweek_first_created_accountweekofyear_first_created_account
206583213441omlc9iku7tFEMALE34basic0endirectdirectlinkedWebMac DesktopChromeES44044201463002720148132332014630027
206584213442rf0ay567js-unknown-49basic0ensem-brandgoogleomgWebMac DesktopChromeNDF546405464201463002720296154242014630027
2065852134430k26r3mir0FEMALE36basic0ensem-brandgooglelinkedWebMac DesktopSafariUS13013201463002720147136282014630027
20658621344440o1ivh6cb-unknown-49basic0endirectdirectlinkedWebWindows DesktopChromeNDF546405464201463002720296154242014630027
206587213445qbxza0xojfFEMALE23basic0ensem-brandgoogleomgWebWindows DesktopIEUS20220146300272014722272014630027
206588213446zxodksqpepMALE32basic0ensem-brandgoogleomgWebMac DesktopSafariNDF546405464201463002720296154242014630027
206589213447mhewnxesx9-unknown-49basic0endirectdirectlinkedWebWindows DesktopChromeNDF546405464201463002720296154242014630027
2065902134486o3arsjbb4-unknown-32basic0endirectdirectuntrackedWebMac DesktopFirefoxNDF546405464201463002720296154242014630027
206591213449jh95kwisub-unknown-49basic25enotherothertracked-otheriOSiPhoneMobile SafariNDF546405464201463002720296154242014630027
206592213450nw9fwlyb5f-unknown-49basic25endirectdirectuntrackediOSiPhone-unknown-NDF546405464201463002720296154242014630027